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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

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Related Experiment Video

Updated: Feb 24, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Peculiar Genes Selection: A new features selection method to improve classification performances in imbalanced data

Federica Martina^1,2, Marco Beccuti¹, Gianfranco Balbo¹

¹Computer Science Department, University of Turin, Turin, Italy.

|August 15, 2017

Summary

This study introduces a novel feature selection method for high-dimensional genomic data, enhancing biomarker discovery. The method significantly improves classification performance on imbalanced datasets, confirming biological relevance.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Area of Science:

Genomics
Bioinformatics
Machine Learning

Background:

High-throughput technologies generate high-dimensional genomic and transcriptomic data.
Classifying samples using this data presents challenges due to data dimensionality and characteristics.
Biomarker detection is crucial for accurate classification in biological studies.

Purpose of the Study:

To develop a novel, three-step feature selection method for identifying class-specific biomarkers.
To address the challenges of high-dimensional data in classification tasks.
To improve classification performance, particularly on imbalanced datasets.

Main Methods:

A three-step feature selection procedure was developed.
Step 1: Detect differentially expressed genes based on experimental design.
Step 2: Filter features with low discriminative power.
Step 3: Identify class-specific features and form a biomarker signature.

Main Results:

The proposed method significantly improved Support Vector Machine classification performance on an imbalanced dataset to 82%, outperforming other methods (max 73%).
Classification performance on a balanced dataset was comparable to existing methods.
Gene Ontology enrichment analysis confirmed the biological relevance of the selected biomarkers.

Conclusions:

The proposed feature selection method effectively identifies class-specific biomarkers from high-dimensional data.
This methodology enhances classification accuracy, especially for imbalanced biological datasets.
The 'PGS' package is available for R users to implement this approach.